K-means Clustering Based Pixel-wise Object Tracking

Pixel-wise object tracking

In this paper, we propose a novel pixel-wise visual object tracking framework that can track any anonymous object in a noisy background. The framework consists of two submodels, a global attention model and a local segmentation model. The global model generates a region of interests (ROI) that the object may lie in the new frame based on the past object segmentation maps; while the local model ...

Object Tracking via Pixel-Wise and Block-Wise Sparse Representation

Superpixels Generating from the Pixel-based K-Means Clustering

Image segmentation is a basic but important preprocessing to image recognition in computer vision applications. In this paper, we propose a pixel-based k-means (PKM) clustering to generate superpixels, which comprise many pixels with similar colors and neighbor positions. In contrast with conventional center-based clustering, the PKM method traces several nearer clustering centers for a pixel i...

Persistent K-Means: Stable Data Clustering Algorithm Based on K-Means Algorithm

Identifying clusters or clustering is an important aspect of data analysis. It is the task of grouping a set of objects in such a way those objects in the same group/cluster are more similar in some sense or another. It is a main task of exploratory data mining, and a common technique for statistical data analysis This paper proposed an improved version of K-Means algorithm, namely Persistent K...

Snipping for robust k-means clustering under component-wise contamination

We introduce the concept of snipping, complementing that of trimming, in robust cluster analysis. An observation is snipped when some of its dimensions are discarded, but the remaining are used for clustering and estimation. Snipped k-means is performed through a probabilistic optimization algorithm which is guaranteed to converge to the global optimum. We show global robustness properties of o...